beachmat 1.4.0
beachmat has a few useful utilities outside of the C++ API. This document describes how to use them.
Given the dimensions of a matrix, users can choose HDF5 chunk dimensions that give fast performance for both row- and column-level access.
library(beachmat)
nrows <- 10000
ncols <- 200
getBestChunkDims(c(nrows, ncols))## [1] 708 15In the future, it should be possible to feed this back into the API.
Currently, if chunk dimensions are not specified in the C++ code, the API will retrieve them from R via the getHDF5DumpChunkDim() function from HDF5Array.
The aim is to also provide a setHDF5DumpChunkDim() function so that any chunk dimension specified in R will be respected.
The most common access patterns for matrices (at least, for high-throughput biological data) is by row or by column.
The rechunkByMargins() will take a HDF5 file and convert it to using purely row- or column-based chunks.
library(HDF5Array)
A <- as(matrix(runif(5000), nrow=100, ncol=50), "HDF5Array")
byrow <- rechunkByMargins(A, byrow=TRUE)
byrow## <100 x 50> HDF5Matrix object of type "double":
## [,1] [,2] [,3] ... [,49] [,50]
## [1,] 0.02368997 0.61867999 0.09901986 . 0.94371845 0.20936938
## [2,] 0.16157399 0.08843818 0.38171039 . 0.47664483 0.01198130
## [3,] 0.31990193 0.79708877 0.95708924 . 0.01796980 0.33201111
## [4,] 0.21734044 0.66196205 0.86554829 . 0.65668447 0.05624321
## [5,] 0.20153414 0.27811737 0.11535485 . 0.71071996 0.91199576
## ... . . . . . .
## [96,] 0.764048594 0.529289348 0.938423564 . 0.45495960 0.77103545
## [97,] 0.096762599 0.144585831 0.001744258 . 0.77428899 0.86295320
## [98,] 0.130667802 0.556895541 0.983594731 . 0.73824354 0.82954800
## [99,] 0.844224918 0.866666283 0.643571294 . 0.20662298 0.02184707
## [100,] 0.182871031 0.985669492 0.982775865 . 0.17300827 0.88982778bycol <- rechunkByMargins(A, byrow=FALSE)
bycol## <100 x 50> HDF5Matrix object of type "double":
## [,1] [,2] [,3] ... [,49] [,50]
## [1,] 0.02368997 0.61867999 0.09901986 . 0.94371845 0.20936938
## [2,] 0.16157399 0.08843818 0.38171039 . 0.47664483 0.01198130
## [3,] 0.31990193 0.79708877 0.95708924 . 0.01796980 0.33201111
## [4,] 0.21734044 0.66196205 0.86554829 . 0.65668447 0.05624321
## [5,] 0.20153414 0.27811737 0.11535485 . 0.71071996 0.91199576
## ... . . . . . .
## [96,] 0.764048594 0.529289348 0.938423564 . 0.45495960 0.77103545
## [97,] 0.096762599 0.144585831 0.001744258 . 0.77428899 0.86295320
## [98,] 0.130667802 0.556895541 0.983594731 . 0.73824354 0.82954800
## [99,] 0.844224918 0.866666283 0.643571294 . 0.20662298 0.02184707
## [100,] 0.182871031 0.985669492 0.982775865 . 0.17300827 0.88982778Rechunking can provide a substantial speed-up to downstream functions, especially those requiring access to random columns or rows.
Indeed, the time saved in those functions often offsets the time spent in constructing a new HDF5Matrix.
sessionInfo()## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] HDF5Array_1.10.0 rhdf5_2.26.0 DelayedArray_0.8.0
## [4] BiocParallel_1.16.0 IRanges_2.16.0 S4Vectors_0.20.0
## [7] BiocGenerics_0.28.0 matrixStats_0.54.0 beachmat_1.4.0
## [10] knitr_1.20 BiocStyle_2.10.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.19 magrittr_1.5 stringr_1.3.1
## [4] tools_3.5.1 xfun_0.4 htmltools_0.3.6
## [7] yaml_2.2.0 rprojroot_1.3-2 digest_0.6.18
## [10] bookdown_0.7 Rhdf5lib_1.4.0 BiocManager_1.30.3
## [13] evaluate_0.12 rmarkdown_1.10 stringi_1.2.4
## [16] compiler_3.5.1 backports_1.1.2